What is Data Mining? Data mining is a business process for exploring large amounts of data to discover patterns and rules (such as “When customers buy flu medication, they also buy soup”). You have probably heard stories about companies using data to recommend movies (e.g., Netflix), identify potential soul mates for people (e.g., eHarmony), and even discover who is pregnant based on credit card receipts (e.g., Target Corp.). But, how does one turn data into these types of insights? That’s what we will be exploring in this course.

How can I benefit from this course? Whether you are interested in accounting, finance, operations, management or marketing—you will benefit from learning how to make decisions driven by data and knowledge rather than hunches and intuition. In fact, the financial sector as a whole, and the accounting sector in particular, are areas which are seeing an extraordinary increase in the volume of information which requires processing, storing and analyzing.

What software will we use? Throughout the semester, we will use real data to work on a variety of data mining problems using a software called RapidMiner. This is an open-source software that can be downloaded for free for Windows, Mac and Linux systems.

What will be the nature of assignments? Assignments will consist of homework problems and a final exam where you will apply the data-analysis techniques covered in class. In addition, there will be a group project and in-class discussions of articles related to big-data. You do not need any extraordinary technical abilities to excel in this course.

What about jobs? There is a growing demand for people skilled in analyzing large datasets. In a report published in May 2011, the McKinsey Global Institute projected that by 2018 the United States could face a shortage of 140,000 to 190,000 workers with “deep analytical” expertise. In addition, they projected a need for 1.5 million more data-savvy managers and analysts, who are capable of asking the right questions and consuming the results of data analysis.

Prerequisites: MIS 301, OM 210, admission to SOM, and an overall GPA of at least 3.0.
(If you have a strong interest in the topic but your GPA is slightly lower than 3.0, send an email to Prof. Sanyal.)

Enrollment Procedure: If you wish to enroll in this course, you must send an email to Prof. Sanyal (psanyal@gmu.edu) expressing your interest with the following information:

Your Full Name:

Your G#:

Your Major (s):

List of MIS and OM courses you have already attended:

Your Overall GPA:

Prof. Sanyal will issue overrides to selected students (maximum 20), who will be then able to register for the course via PatriotWeb.

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